




'''simple docstring'''







import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     StableDiffusionPipeline.from_pretrained(UpperCAmelCase      ,torch_dtype=torch.floataa						)

   # load LoRA weight from .safetensors
   A__       =     load_file(UpperCAmelCase						)

   A__       =     []

   # directly update weight in diffusers model
   for key in state_dict:
      # it is suggested to print out the key, it usually will be something like below
      # "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"

      # as we have set the alpha beforehand, so just skip
      if ".alpha" in key or key in visited:
         continue

      if "text" in key:
         A__       =     key.split('.'						)[0].split(LORA_PREFIX_TEXT_ENCODER + '_'						)[-1].split('_'						)
         A__       =     pipeline.text_encoder
      else:
         A__       =     key.split('.'						)[0].split(LORA_PREFIX_UNET + '_'						)[-1].split('_'						)
         A__       =     pipeline.unet

      # find the target layer
      A__       =     layer_infos.pop(0						)
      while len(UpperCAmelCase						) > -1:
         try:
            A__       =     curr_layer.__getattr__(UpperCAmelCase						)
            if len(UpperCAmelCase						) > 0:
               A__       =     layer_infos.pop(0						)
            elif len(UpperCAmelCase						) == 0:
               break
         except Exception:
            if len(UpperCAmelCase						) > 0:
               temp_name += "_" + layer_infos.pop(0						)
            else:
               A__       =     layer_infos.pop(0						)

      A__       =     []
      if "lora_down" in key:
         pair_keys.append(key.replace('lora_down'      ,'lora_up'						)						)
         pair_keys.append(UpperCAmelCase						)
      else:
         pair_keys.append(UpperCAmelCase						)
         pair_keys.append(key.replace('lora_up'      ,'lora_down'						)						)

      # update weight
      if len(state_dict[pair_keys[0]].shape						) == 4:
         A__       =     state_dict[pair_keys[0]].squeeze(3						).squeeze(2						).to(torch.floataa						)
         A__       =     state_dict[pair_keys[1]].squeeze(3						).squeeze(2						).to(torch.floataa						)
         curr_layer.weight.data += alpha * torch.mm(UpperCAmelCase      ,UpperCAmelCase						).unsqueeze(2						).unsqueeze(3						)
      else:
         A__       =     state_dict[pair_keys[0]].to(torch.floataa						)
         A__       =     state_dict[pair_keys[1]].to(torch.floataa						)
         curr_layer.weight.data += alpha * torch.mm(UpperCAmelCase      ,UpperCAmelCase						)

      # update visited list
      for item in pair_keys:
         visited.append(UpperCAmelCase						)

   return pipeline


if __name__ == "__main__":
    lowerCAmelCase_     						=				argparse.ArgumentParser()

    parser.add_argument(
        '''--base_model_path''', default=None, type=str, required=True, help='''Path to the base model in diffusers format.'''
    )
    parser.add_argument(
        '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
    )
    parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
    parser.add_argument(
        '''--lora_prefix_unet''', default='''lora_unet''', type=str, help='''The prefix of UNet weight in safetensors'''
    )
    parser.add_argument(
        '''--lora_prefix_text_encoder''',
        default='''lora_te''',
        type=str,
        help='''The prefix of text encoder weight in safetensors''',
    )
    parser.add_argument('''--alpha''', default=0.75, type=float, help='''The merging ratio in W = W0 + alpha * deltaW''')
    parser.add_argument(
        '''--to_safetensors''', action='''store_true''', help='''Whether to store pipeline in safetensors format or not.'''
    )
    parser.add_argument('''--device''', type=str, help='''Device to use (e.g. cpu, cuda:0, cuda:1, etc.)''')

    lowerCAmelCase_     						=				parser.parse_args()

    lowerCAmelCase_     						=				args.base_model_path
    lowerCAmelCase_     						=				args.checkpoint_path
    lowerCAmelCase_     						=				args.dump_path
    lowerCAmelCase_     						=				args.lora_prefix_unet
    lowerCAmelCase_     						=				args.lora_prefix_text_encoder
    lowerCAmelCase_     						=				args.alpha

    lowerCAmelCase_     						=				convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

    lowerCAmelCase_     						=				pipe.to(args.device)
    pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)








'''simple docstring'''







import inspect
import unittest
import warnings

from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch
    from torch import nn

    from transformers import (
        MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
        MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
        MODEL_MAPPING,
        DeiTForImageClassification,
        DeiTForImageClassificationWithTeacher,
        DeiTForMaskedImageModeling,
        DeiTModel,
    )
    from transformers.models.deit.modeling_deit import DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import DeiTImageProcessor

class 	_snake_case:






 def __init__(self       :  List[Any]    ,		a       :  str    ,		a       :  Any=13    ,		a       :  Optional[Any]=30    ,		a       :  Union[str, Any]=2    ,		a       :  List[str]=3    ,		a       :  List[str]=True    ,		a       :  List[Any]=True    ,		a       :  Tuple=32    ,		a       :  Optional[Any]=5    ,		a       :  str=4    ,		a       :  List[str]=37    ,		a       :  List[str]="gelu"    ,		a       :  int=0.1    ,		a       :  int=0.1    ,		a       :  str=10    ,		a       :  Tuple=0.02    ,		a       :  Union[str, Any]=3    ,		a       :  List[str]=None    ,		a       :  Any=2    ,		)       ->   Optional[int]:






    """simple docstring"""


    A__       =     parent
    A__       =     batch_size
    A__       =     image_size
    A__       =     patch_size
    A__       =     num_channels
    A__       =     is_training
    A__       =     use_labels
    A__       =     hidden_size
    A__       =     num_hidden_layers
    A__       =     num_attention_heads
    A__       =     intermediate_size
    A__       =     hidden_act
    A__       =     hidden_dropout_prob
    A__       =     attention_probs_dropout_prob
    A__       =     type_sequence_label_size
    A__       =     initializer_range
    A__       =     scope
    A__       =     encoder_stride

    # in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
    A__       =     (image_size // patch_size) ** 2
    A__       =     num_patches + 2






 def 						_UpperCamelCase (self       :  Optional[Any]    )       ->   List[str]:






    """simple docstring"""


    A__       =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

    A__       =     None
    if self.use_labels:
       A__       =     ids_tensor([self.batch_size]    ,		self.type_sequence_label_size    )

    A__       =     self.get_config()

    return config, pixel_values, labels






 def 						_UpperCamelCase (self       :  Union[str, Any]    )       ->   int:






    """simple docstring"""


    return DeiTConfig(
        image_size=self.image_size    ,		patch_size=self.patch_size    ,		num_channels=self.num_channels    ,		hidden_size=self.hidden_size    ,		num_hidden_layers=self.num_hidden_layers    ,		num_attention_heads=self.num_attention_heads    ,		intermediate_size=self.intermediate_size    ,		hidden_act=self.hidden_act    ,		hidden_dropout_prob=self.hidden_dropout_prob    ,		attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,		is_decoder=a    ,		initializer_range=self.initializer_range    ,		encoder_stride=self.encoder_stride    ,		)






 def 						_UpperCamelCase (self       :  Optional[int]    ,		a       :  Tuple    ,		a       :  Dict    ,		a       :  Optional[Any]    )       ->   Optional[Any]:






    """simple docstring"""


    A__       =     DeiTModel(config=a    )
    model.to(a    )
    model.eval()
    A__       =     model(a    )
    self.parent.assertEqual(result.last_hidden_state.shape    ,		(self.batch_size, self.seq_length, self.hidden_size)    )






 def 						_UpperCamelCase (self       :  Optional[int]    ,		a       :  Any    ,		a       :  Optional[Any]    ,		a       :  Optional[int]    )       ->   Dict:






    """simple docstring"""


    A__       =     DeiTForMaskedImageModeling(config=a    )
    model.to(a    )
    model.eval()
    A__       =     model(a    )
    self.parent.assertEqual(
        result.reconstruction.shape    ,		(self.batch_size, self.num_channels, self.image_size, self.image_size)    )

    # test greyscale images
    A__       =     1
    A__       =     DeiTForMaskedImageModeling(a    )
    model.to(a    )
    model.eval()

    A__       =     floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
    A__       =     model(a    )
    self.parent.assertEqual(result.reconstruction.shape    ,		(self.batch_size, 1, self.image_size, self.image_size)    )






 def 						_UpperCamelCase (self       :  Optional[Any]    ,		a       :  Optional[Any]    ,		a       :  Union[str, Any]    ,		a       :  Union[str, Any]    )       ->   str:






    """simple docstring"""


    A__       =     self.type_sequence_label_size
    A__       =     DeiTForImageClassification(a    )
    model.to(a    )
    model.eval()
    A__       =     model(a    ,		labels=a    )
    self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, self.type_sequence_label_size)    )

    # test greyscale images
    A__       =     1
    A__       =     DeiTForImageClassification(a    )
    model.to(a    )
    model.eval()

    A__       =     floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
    A__       =     model(a    ,		labels=a    )
    self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, self.type_sequence_label_size)    )






 def 						_UpperCamelCase (self       :  List[Any]    )       ->   Optional[int]:






    """simple docstring"""


    A__       =     self.prepare_config_and_inputs()
    (
        (
        A__
    )  ,			(
        A__
    )  ,			(
        A__
    )  ,			
    )       =     config_and_inputs
    A__       =     {'pixel_values': pixel_values}
    return config, inputs_dict




@require_torch
class 	_snake_case(			UpperCAmelCase       , UpperCAmelCase       , unittest.TestCase      ):
 __snake_case:							Optional[Any]						=					(
     (
         DeiTModel,
         DeiTForImageClassification,
         DeiTForImageClassificationWithTeacher,
         DeiTForMaskedImageModeling,
     )
     if is_torch_available()
     else ()
 )
 __snake_case:							int						=					(
     {
         '''feature-extraction''': DeiTModel,
         '''image-classification''': (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
     }
     if is_torch_available()
     else {}
 )

 __snake_case:							Any						=					False
 __snake_case:							Any						=					False
 __snake_case:							Any						=					False






 def 						_UpperCamelCase (self       :  Any    )       ->   int:






    """simple docstring"""


    A__       =     DeiTModelTester(self    )
    A__       =     ConfigTester(self    ,		config_class=a    ,		has_text_modality=a    ,		hidden_size=37    )






 def 						_UpperCamelCase (self       :  Dict    )       ->   Tuple:






    """simple docstring"""


    self.config_tester.run_common_tests()






 @unittest.skip(reason='DeiT does not use inputs_embeds'    )
 def 						_UpperCamelCase (self       :  List[str]    )       ->   List[str]:






    """simple docstring"""


    pass






 def 						_UpperCamelCase (self       :  Optional[Any]    )       ->   Tuple:






    """simple docstring"""


    A__  ,			A__       =     self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
       A__       =     model_class(a    )
       self.assertIsInstance(model.get_input_embeddings()    ,		(nn.Module)    )
       A__       =     model.get_output_embeddings()
       self.assertTrue(x is None or isinstance(a    ,		nn.Linear    )    )






 def 						_UpperCamelCase (self       :  Union[str, Any]    )       ->   str:






    """simple docstring"""


    A__  ,			A__       =     self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
       A__       =     model_class(a    )
       A__       =     inspect.signature(model.forward    )
       # signature.parameters is an OrderedDict => so arg_names order is deterministic
       A__       =     [*signature.parameters.keys()]

       A__       =     ['pixel_values']
       self.assertListEqual(arg_names[:1]    ,		a    )






 def 						_UpperCamelCase (self       :  Union[str, Any]    )       ->   Dict:






    """simple docstring"""


    A__       =     self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*a    )






 def 						_UpperCamelCase (self       :  Dict    )       ->   Any:






    """simple docstring"""


    A__       =     self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_masked_image_modeling(*a    )






 def 						_UpperCamelCase (self       :  List[Any]    )       ->   Optional[Any]:






    """simple docstring"""


    A__       =     self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_image_classification(*a    )






 def 						_UpperCamelCase (self       :  Optional[int]    ,		a       :  int    ,		a       :  Union[str, Any]    ,		a       :  List[Any]=False    )       ->   Optional[int]:






    """simple docstring"""


    A__       =     super()._prepare_for_class(a    ,		a    ,		return_labels=a    )

    if return_labels:
       if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
          del inputs_dict["labels"]

    return inputs_dict






 def 						_UpperCamelCase (self       :  Any    )       ->   Tuple:






    """simple docstring"""


    if not self.model_tester.is_training:
       return

    A__  ,			A__       =     self.model_tester.prepare_config_and_inputs_for_common()
    A__       =     True

    for model_class in self.all_model_classes:
       # DeiTForImageClassificationWithTeacher supports inference-only
       if (
           model_class in get_values(a    )
           or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
       ):
          continue
       A__       =     model_class(a    )
       model.to(a    )
       model.train()
       A__       =     self._prepare_for_class(a    ,		a    ,		return_labels=a    )
       A__       =     model(**a    ).loss
       loss.backward()






 def 						_UpperCamelCase (self       :  Optional[Any]    )       ->   int:






    """simple docstring"""


    A__  ,			A__       =     self.model_tester.prepare_config_and_inputs_for_common()
    if not self.model_tester.is_training:
       return

    A__       =     False
    A__       =     True

    for model_class in self.all_model_classes:
       if model_class in get_values(a    ) or not model_class.supports_gradient_checkpointing:
          continue
       # DeiTForImageClassificationWithTeacher supports inference-only
       if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
          continue
       A__       =     model_class(a    )
       model.gradient_checkpointing_enable()
       model.to(a    )
       model.train()
       A__       =     self._prepare_for_class(a    ,		a    ,		return_labels=a    )
       A__       =     model(**a    ).loss
       loss.backward()






 def 						_UpperCamelCase (self       :  Optional[Any]    )       ->   List[str]:






    """simple docstring"""


    A__  ,			A__       =     self.model_tester.prepare_config_and_inputs_for_common()

    A__       =     [
        {'title': 'multi_label_classification', 'num_labels': 2, 'dtype': torch.float},
        {'title': 'single_label_classification', 'num_labels': 1, 'dtype': torch.long},
        {'title': 'regression', 'num_labels': 1, 'dtype': torch.float},
    ]

    for model_class in self.all_model_classes:
       if (
           model_class
           not in [
               *get_values(a    ),
               *get_values(a    ),
           ]
           or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
       ):
          continue

       for problem_type in problem_types:
          with self.subTest(msg=f"""Testing {model_class} with {problem_type["title"]}"""    ):
             A__       =     problem_type['title']
             A__       =     problem_type['num_labels']

             A__       =     model_class(a    )
             model.to(a    )
             model.train()

             A__       =     self._prepare_for_class(a    ,		a    ,		return_labels=a    )

             if problem_type["num_labels"] > 1:
                A__       =     inputs['labels'].unsqueeze(1    ).repeat(1    ,		problem_type['num_labels']    )

             A__       =     inputs['labels'].to(problem_type['dtype']    )

             # This tests that we do not trigger the warning form PyTorch "Using a target size that is different
             # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
             # they have the same size." which is a symptom something in wrong for the regression problem.
             # See https://github.com/huggingface/transformers/issues/11780
             with warnings.catch_warnings(record=a    ) as warning_list:
                A__       =     model(**a    ).loss
             for w in warning_list:
                if "Using a target size that is different to the input size" in str(w.message    ):
                   raise ValueError(
                       f"""Something is going wrong in the regression problem: intercepted {w.message}"""    )

             loss.backward()






 @slow
 def 						_UpperCamelCase (self       :  Union[str, Any]    )       ->   Tuple:






    """simple docstring"""


    for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       A__       =     DeiTModel.from_pretrained(a    )
       self.assertIsNotNone(a    )







def 						_A      ( ):






   '''simple docstring'''







   A__       =     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
   return image







@require_torch
@require_vision
class 	_snake_case(			unittest.TestCase      ):






 @cached_property
 def 						_UpperCamelCase (self       :  Tuple    )       ->   Union[str, Any]:






    """simple docstring"""


    return (
        DeiTImageProcessor.from_pretrained('facebook/deit-base-distilled-patch16-224'    )
        if is_vision_available()
        else None
    )






 @slow
 def 						_UpperCamelCase (self       :  List[str]    )       ->   Optional[Any]:






    """simple docstring"""


    A__       =     DeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-224'    ).to(
        a    )

    A__       =     self.default_image_processor
    A__       =     prepare_img()
    A__       =     image_processor(images=a    ,		return_tensors='pt'    ).to(a    )

    # forward pass
    with torch.no_grad():
       A__       =     model(**a    )

    # verify the logits
    A__       =     torch.Size((1, 10_00)    )
    self.assertEqual(outputs.logits.shape    ,		a    )

    A__       =     torch.tensor([-1.0266, 0.1912, -1.2861]    ).to(a    )

    self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,		a    ,		atol=1e-4    )    )






 @slow
 @require_accelerate
 @require_torch_gpu
 def 						_UpperCamelCase (self       :  Tuple    )       ->   str:






    """simple docstring"""


    A__       =     DeiTModel.from_pretrained(
        'facebook/deit-base-distilled-patch16-224'    ,		torch_dtype=torch.floataa    ,		device_map='auto'    )
    A__       =     self.default_image_processor

    A__       =     prepare_img()
    A__       =     image_processor(images=a    ,		return_tensors='pt'    )
    A__       =     inputs.pixel_values.to(a    )

    # forward pass to make sure inference works in fp16
    with torch.no_grad():
       A__       =     model(a    )


